Multi-Agent Financial Risk Intelligence Platform

Overview

The Multi-Agent Financial Risk Intelligence Platform is an AI-powered financial analytics system designed to provide portfolio insights, risk assessment, market sentiment analysis, and investment recommendations. The platform leverages real-time financial data, MCP (Model Context Protocol), and multiple specialized agents to simulate an intelligent financial assistant.

The system combines quantitative risk metrics such as Value at Risk (VaR) and Sharpe Ratio with qualitative insights from real-time financial news and sentiment analysis to deliver actionable recommendations.

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Features

• Portfolio Value Analysis
• Stock Allocation Analysis
• Sector Allocation Analysis
• Volatility Analysis
• Value at Risk (VaR) Calculation
• Sharpe Ratio Computation
• Real-Time Financial News Aggregation
• Sentiment Analysis using TextBlob
• Recommendation Agent
• Chart Generation using Matplotlib
• CSV Report Generation
• MCP Server Integration
• Streamlit Dashboard

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Project Architecture

                    User
                      |
                      v
            Streamlit Dashboard
                      |
                      v
       Multi-Agent Financial System
                      |
    ------------------------------------------------
    |              |              |               |
Portfolio      Risk Agent     News Agent    MCP Server
 Agent             |               |              |
    |               |               |              |
    |           VaR Agent      Sentiment Agent     |
    |               |               |              |
    ------------------------------------------------
                      |
                      v
            Recommendation Agent
                      |
                      v
               Final Risk Report

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Multi-Agent Workflow

Portfolio CSV
      |
      v
Portfolio Analysis
      |
      v
Volatility Analysis
      |
      v
Value at Risk (VaR)
      |
      v
Sharpe Ratio
      |
      v
News Agent
      |
      v
Sentiment Agent
      |
      v
Recommendation Agent
      |
      v
CSV Report + Dashboard

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MCP Tools

The Financial Risk MCP Server exposes the following tools:

• get_portfolio_value()
• get_sharpe_ratio()
• get_var()
• get_sentiment()
• get_latest_news()
• get_recommendation()

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Tech Stack

Category	Technologies
Language	Python
Data Processing	Pandas, NumPy
Financial Data	yFinance
NLP	TextBlob
Visualization	Matplotlib
Dashboard	Streamlit
MCP	FastMCP
News Source	Google News RSS
Reporting	CSV

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Financial Metrics Implemented

Value at Risk (VaR)

Measures the maximum expected loss over a given period at a specified confidence level.

VaR = Mean Return - (Z-Score × Standard Deviation)

Sharpe Ratio

Evaluates risk-adjusted returns.

Sharpe Ratio =
(Average Return - Risk Free Rate)
                /
            Volatility

Volatility

Measures the variability of stock returns.

Volatility = Standard Deviation of Daily Returns

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Folder Structure

multi-agent-financial-risk-intelligence-platform/
|
├── data/
│   └── portfolio.csv
|
├── charts/
│   ├── portfolio_allocation.png
│   ├── sector_allocation.png
│   └── daily_returns.png
|
├── dashboard/
│   └── app.py
|
├── reports/
│   └── report.csv
|
├── main.py
├── server.py
├── README.md
└── requirements.txt

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Installation

Clone the repository:

git clone https://github.com/your-username/multi-agent-financial-risk-intelligence-platform.git

cd multi-agent-financial-risk-intelligence-platform

Install dependencies:

pip install -r requirements.txt

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Running the Project

Run Portfolio Analytics

python main.py

Run MCP Server

python server.py

Run Streamlit Dashboard

streamlit run dashboard/app.py

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Sample Output

Metric	Value
Portfolio Value	$98,052
Sharpe Ratio	0.13
Value at Risk	$2,532
Sentiment	Positive

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Key Achievements

• Developed an end-to-end financial risk analytics platform.
• Implemented multiple specialized agents for risk assessment and market analysis.
• Integrated MCP (Model Context Protocol) to expose financial tools.
• Leveraged real-time financial data using Yahoo Finance and Google News RSS.
• Built an interactive dashboard for portfolio visualization and monitoring.
• Generated actionable investment recommendations using quantitative and qualitative analysis.

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Conclusion

The Multi-Agent Financial Risk Intelligence Platform demonstrates the integration of financial analytics, artificial intelligence, multi-agent systems, and MCP-based tool orchestration to build an intelligent financial risk assessment solution.

The project provides a comprehensive view of portfolio performance by combining quantitative financial metrics with qualitative market intelligence, making it a practical application of AI in the financial domain.